Enhancing Active Learning for Sentinel 2 Imagery through Contrastive Learning and Uncertainty Estimation
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917704261173248 |
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| author | Pogorzelski, David Arlinghaus, Peter Zhang, Wenyan |
| author_facet | Pogorzelski, David Arlinghaus, Peter Zhang, Wenyan |
| contents | In this paper, we introduce a novel method designed to enhance label efficiency in satellite imagery analysis by integrating semi-supervised learning (SSL) with active learning strategies. Our approach utilizes contrastive learning together with uncertainty estimations via Monte Carlo Dropout (MC Dropout), with a particular focus on Sentinel-2 imagery analyzed using the Eurosat dataset. We explore the effectiveness of our method in scenarios featuring both balanced and unbalanced class distributions. Our results show that the proposed method performs better than several other popular methods in this field, enabling significant savings in labeling effort while maintaining high classification accuracy. These findings highlight the potential of our approach to facilitate scalable and cost-effective satellite image analysis, particularly advantageous for extensive environmental monitoring and land use classification tasks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_13285 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Active Learning for Sentinel 2 Imagery through Contrastive Learning and Uncertainty Estimation Pogorzelski, David Arlinghaus, Peter Zhang, Wenyan Computer Vision and Pattern Recognition Machine Learning In this paper, we introduce a novel method designed to enhance label efficiency in satellite imagery analysis by integrating semi-supervised learning (SSL) with active learning strategies. Our approach utilizes contrastive learning together with uncertainty estimations via Monte Carlo Dropout (MC Dropout), with a particular focus on Sentinel-2 imagery analyzed using the Eurosat dataset. We explore the effectiveness of our method in scenarios featuring both balanced and unbalanced class distributions. Our results show that the proposed method performs better than several other popular methods in this field, enabling significant savings in labeling effort while maintaining high classification accuracy. These findings highlight the potential of our approach to facilitate scalable and cost-effective satellite image analysis, particularly advantageous for extensive environmental monitoring and land use classification tasks. |
| title | Enhancing Active Learning for Sentinel 2 Imagery through Contrastive Learning and Uncertainty Estimation |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2405.13285 |